Variance Analysis in an Observability Platform

1 . A computer-implemented method comprising:

receiving a request to perform a variance analysis in association with a user-selected dataset on an observability platform, the request including user input defining the user-selected dataset including at least one user-selected datapoint identified by the user input;

generating an outlier representative sample based on data representing the user-selected dataset and the at least one user-selected datapoint;

generating a baseline representative sample based on the data representing the user-selected dataset;

causing the variance analysis to be performed based on the at least one user-selected datapoint, the baseline representative sample, and the outlier representative sample;

generating a plurality of data visualizations based on a result data set of the variance analysis and a description of the user-selected dataset based on the user input;

ranking the plurality of data visualizations based on the result data set including one or more disparity data metrics; and

providing the plurality of data visualizations and the description of the user-selected dataset for display in an order based on the ranking on the observability platform as a response to the request.

2 . The computer-implemented method of claim 1 , further comprising:

receiving user feedback on the response to the request on the observability platform, the user feedback identifying a dataset; and

modifying a query associated with the result data set to include a filter based on the user feedback associated with at least one of the one or more disparity data metrics, wherein the filter is based on the dataset identified by the user feedback.

3 . The computer-implemented method of claim 1 , wherein the baseline representative sample and the outlier representative sample are generated based on randomly selecting a predetermined set of values.

4 . The computer-implemented method of claim 1 , wherein the variance analysis is performed using an application programming interface call to a web service communicatively coupled to a variance analysis engine.

5 . The computer-implemented method of claim 1 , wherein the result data set of the variance analysis comprises a plurality of data dimensions with a corresponding plurality of data values that detail the one or more disparity data metrics.

6 . The computer-implemented method of claim 5 , wherein the variance analysis comprises:

comparing the baseline representative sample to the outlier representative sample across all data dimensions in the user-selected dataset retrieved from the observability platform;

determining the plurality of data dimensions with the corresponding plurality of data values based on one or more disparity thresholds; and

generating the one or more disparity data metrics based on the plurality of data values between the baseline representative sample and the outlier representative sample.

7 . The computer-implemented method of claim 1 , wherein the variance analysis uses a heuristic rules engine to determine the result data set including the one or more disparity data metrics.

8 . A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform operations including:

receiving a request to perform a variance analysis in association with a user-selected dataset on an observability platform, the request including user input defining the user-selected dataset including at least one user-selected datapoint identified by the user input;

generating an outlier representative sample based on data representing the user-selected dataset and the at least one user-selected datapoint;

generating a baseline representative sample based on the data representing the user-selected dataset;

causing the variance analysis to be performed based on the at least one user-selected datapoint, the baseline representative sample, and the outlier representative sample;

generating a plurality of data visualizations based on a result data set of the variance analysis and a description of the user-selected dataset based on the user input;

ranking the plurality of data visualizations based on the result data set including one or more disparity data metrics; and

providing the plurality of data visualizations and the description of the user-selected dataset for display in an order based on the ranking on the observability platform as a response to the request.

9 . The system of claim 8 , wherein the operations further comprise:

receiving user feedback on the response to the request on the observability platform, the user feedback identifying a dataset; and

modifying a query associated with the result data set to include a filter based on the user feedback associated with at least one of the one or more disparity data metrics, wherein the filter is based on the dataset identified by the user feedback.

10 . The system of claim 8 , wherein the baseline representative sample is generated based on randomly selecting a predetermined set of values.

11 . The system of claim 8 , wherein the variance analysis is performed using an application programming interface call to a web service communicatively coupled to a variance analysis engine.

12 . The system of claim 8 , wherein the result data set of the variance analysis comprises a plurality of data dimensions with a corresponding plurality of data values that detail the one or more disparity data metrics.

13 . The system of claim 12 , wherein the variance analysis comprises:

comparing the baseline representative sample to the outlier representative sample across all data dimensions in the user-selected dataset retrieved from the observability platform;

determining the plurality of data dimensions with the corresponding plurality of data values based on one or more disparity thresholds; and

generating the one or more disparity data metrics based on the plurality of data values between the baseline representative sample and the outlier representative sample.

14 . The system of claim 8 , wherein the variance analysis uses a heuristic rules engine to determine the result data set including the one or more disparity data metrics.

15 . One or more non-transitory computer-readable storage media storing one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to:

receive a request to perform a variance analysis in association with a user-selected dataset on an observability platform, the request including user input defining the user-selected dataset including at least one user-selected datapoint identified by the user input;

generate an outlier representative sample based on data representing the user-selected dataset and the at least one user-selected datapoint;

generate a baseline representative sample based on the data representing user-selected dataset;

cause the variance analysis to be performed based on the at least one user-selected datapoint, the baseline representative sample, and the outlier representative sample;

generate a plurality of data visualizations based on a result data set of the variance analysis and a description of the user-selected dataset based on the user input;

rank the plurality of data visualizations based on the result data set including one or more disparity data metrics; and

provide the plurality of data visualizations and the description of the user-selected dataset for display in an order based on the ranking on the observability platform as a response to the request.

16 . The one or more non-transitory computer-readable storage media of claim 15 , wherein the one or more processors are further caused to:

receive user feedback on the response to the request on the observability platform, the user feedback identifying a dataset; and

modify a query associated with the result data set to include a filter based on the user feedback associated with at least one of the one or more disparity data metrics, wherein the filter is based on the dataset identified by the user feedback.

17 . The one or more non-transitory computer-readable storage media of claim 15 , wherein the baseline representative sample is generated based on randomly selecting a predetermined set of values.

18 . The one or more non-transitory computer-readable storage media of claim 15 , wherein the result data set of the variance analysis comprises a plurality of data dimensions with a corresponding plurality of data values that detail the one or more disparity data metrics.

19 . The one or more non-transitory computer-readable storage media of claim 18 , wherein the variance analysis comprises:

comparing the baseline representative sample to the outlier representative sample across all data dimensions in the user-selected dataset retrieved from the observability platform;

determining the plurality of data dimensions with the corresponding plurality of data values based on one or more disparity thresholds; and

generating the one or more disparity data metrics based on the plurality of data values between the baseline representative sample and the outlier representative sample.

20 . The one or more non-transitory computer-readable storage media of claim 15 , wherein the variance analysis uses a heuristic rules engine to determine the result data set including the one or more disparity data metrics.